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This video essay discusses the pervasive nature of digital surveillance and its origins in the partnership between Silicon Valley and US intelligence agencies.

Part 1: The Surveillance State

The video begins by highlighting the sheer number of cameras in New York City and how easily individuals can be tracked. It points out that law enforcement agencies, like the NYPD, use facial recognition technology in conjunction with these cameras, raising concerns about privacy and potential misuse, particularly against marginalized groups like BLM protestors. Beyond cameras, the video lists various other surveillance tools used by police, such as body cams, drones, license plate readers, fake cell towers, and more. The video then shifts to surveillance by private companies. It provides an example of a French app using inaudible sounds to track user location and emphasizes the vast data collection practices of tech giants like Google, Facebook, and Amazon. Google’s extensive tracking infrastructure across websites is highlighted, along with the company’s ability to collect detailed user data. The video reveals how this data is shared with agencies like the FBI and NSA through programs like PRISM, Boundless Informant, and XKeyscore, often without warrants.

Part 2: The Origins of Surveillance Capitalism

The video traces the history of the surveillance state back to the CIA’s backing of In-Q-Tel, a venture capital firm, in 1999. Initially intended as a temporary project, In-Q-Tel gained prominence after 9/11 and became a key player in connecting Silicon Valley startups with intelligence agencies. Before 9/11, there was growing public concern about internet privacy and the government was considering regulations to protect user data. However, the Patriot Act, passed after 9/11, effectively dismantled these efforts and prioritized national security over privacy. This shift created a symbiotic relationship between tech companies and intelligence agencies. Google, in particular, benefited from this partnership, securing contracts with the NSA and CIA, and acquiring Keyhole (which became Google Earth).

Part 3: Google’s Transformation

The video explains how Google, initially struggling to generate revenue, discovered the potential of user data for targeted advertising. Amit Patel’s discovery in 2001 that Google was inadvertently collecting vast amounts of user data during searches led to a strategic shift. This data became the foundation for detailed behavioral profiles, attracting advertisers and financial institutions. This coincided with the post-9/11 environment, which provided funding and justification for data mining projects. Google’s focus shifted from being just a search engine to a company centered on collecting personal information. The video argues that Google’s success was not solely due to its free services but rather its exploitation of user data, facilitated by government support and the dismantling of privacy regulations. This data is now used not just for advertising but also for assessing creditworthiness based on seemingly innocuous details.

Part 4: The Illusion of Security and the Cost of Privacy

The video debunks the claim that increased surveillance equates to increased security, citing studies that show the ineffectiveness of mass surveillance in preventing terrorism. It argues that surveillance is not a necessary trade-off for free services but rather a profitable business model built on exploiting personal data. The video concludes by emphasizing the importance of privacy for individual safety, freedom of expression, and challenging power structures. It warns against the normalization of surveillance and urges viewers to consider the potential consequences of constant data collection. The video promotes Means TV, an anti-capitalist streaming service, as an alternative platform for creators to produce content without the constraints and censorship of YouTube.

What about play-integrity?

Billions of people have been using Google for years, and Google has been using artificial intelligence for years. It’s nothing new.

I use open-source applications that don’t require internet access for tasks that can be done perfectly offline, which is great not just for privacy but for many other reasons as well.

Interesting, I wish there was something like this for Twitter

No matter what, it helps me incredibly.

Language models are improving so quickly that we’re starting to take their amazing abilities for granted. Tasks that used to seem really difficult for computers, like understanding complex language or writing creatively, now feel normal and expected. We’re getting used to this advanced technology and forgetting how impressive it truly is.